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The issue of the dss adoption process and its complexity

As many organizations started to upgrade their network infrastructure, object oriented technology and data warehousing started to make its mark on Decision Support Systems. The rapid expansion of the Internet provided additional opportunities for the scope of Decision Support Systems and consequently many new innovative systems such as OLAP and other web-drive systems were developed.

They define DSS broadly as an interactive computer based system that help decision-makers use data and models to solve ill-structured, unstructured or semi-structured problems.

Managers main uses for a DSS includes searching, retrieving and analyzing decision relevant data to allow them to summarize main points which assist them in making more informed and educated decisions. In addition to that, DSS can take on many different forms and can be used in many different ways, i. Thus, in order to comprehend the intricate complexities of what services a DSS can provide, we first have to look at the stand-alone units that supports the DSS.

  • Decision support was found to be used more often for less complex decisions;
  • Web-based DSS are computerized systems that delivers decision support information or decision support tools to a manager or business analyst using a "thin-client" Web browser like Netscape Navigator or Internet Explorer;
  • It is a common notion that information can be and often is… misinterpreted, leading to inaccurate conclusions which adversely affects the quality of the decision making process inside an organization as is humoristically depicted in figure 1…;
  • There are multiple factors that qualify information as having good quality such as timeliness, relevant, accurateness, consistency, unbiased, etc;
  • Knowledge driven DSS are often referred to as management expert systems or intelligent decision support systems;
  • As many organizations started to upgrade their network infrastructure, object oriented technology and data warehousing started to make its mark on Decision Support Systems.

Although DSS can be dissected into many different components, I will mainly concentrate on a few important aspects of its design. Having summarized the most important functions of a DSS, it should be remembered that a DSS is only as good as the individual components that it consist of: DSS is built on top of a transaction system, a database and a data model, all of which provides the DSS with data and information that is processed and presented to the user in a simplified form.

The fist important aspect of DSS is that they provide information which are used in the decision making process. The emphasis here is not on the quantity of information, but rather the quality.

There are multiple factors that qualify information as having good quality such as timeliness, relevant, accurateness, consistency, unbiased, etc. It is a common notion that information can be and often is… misinterpreted, leading to inaccurate conclusions which adversely affects the quality of the decision making process inside an organization as is humoristically depicted in figure 1….

They falsely rely on the assumption that information should give them guidance, in stead of realizing that their understanding of relevant information will help them formulate their own ideas, which should be based on both a intricate understanding of the information, the necessary knowledge available around them and ultimately their intuition that is develop based on experience.

To make a good decision, one needs not only information about the specific instance, but also an understanding of the domain. In other words, one needs a set of principles, models, templates or other abstractions.

Better understanding enables the identification of what information is relevant and consequently, less information is required because the irrelevant components can be ignored. This not only decreases the complexity of the decision process, but also decreases the processing load on the manager and leaves him with more time to focus on critical and situation-relevant information segments.

These abstractions are then re-usable for making new decisions with different information, facilitating the process of knowledge management and ultimately enhancing the overall quality of decision making inside the organization. Unlike information, which often relates only to specific instances, knowledge is contend-rich and re-usable and should thus be captured whenever possible to provide a point of reference for future similar scenarios.

Amongst the common ones are the following: With model drive DSS the emphasize is on access to and manipulation of a model, rather than data, i.

These systems usually are not data intensive and consequently are not linked to very large databases. Knowledge driven DSS are often referred to as management expert systems or intelligent decision support systems.

They focuses on knowledge and recommends actions to managers based on an analysis of a certain knowledge base. Moreover, it has special problem solving expertise and are closely related to data mining i.

Decision Complexity Affects the Extent and Type of Decision Support Use

Document driven These systems help managers retrieve and mange unstructured documents and web pages by integrating a variety of storage and processing technologies to provide complete document retrieval and analysis. It also access documents such as company policies and procedures, product specification, catalogs, corporate historical documents, minutes of meetings, important correspondence, corporate records, etc.

They are a special type of hybrid DSS that emphasizes the use of communications and decision models intended to facilitate the solution of problems by decision makers working together as a group.

  • At lower management levels, information emphasis is internally generated and relies on short-term goals;
  • Prescribing decisions of higher complexity were associated with a lower frequency of DSS use, but required the use of the more cognitively demanding situation assessment tool for infection risk along with pathology data.

GDSS supports electronic communication, scheduling, document sharing and other group productivity and decision enhancing activities and involves technologies such as two-way interactive video, bulletin boards, e-mail, etc.

See table 1 in appendix section for a overview of an expanded Decision Support Systems framework 2. Inter-organization DSS are used to serve companies stakeholders customers, suppliers, etc. The latter, because of their stricter control, are often stand-alone units inside the firm. A very popular example is Web based DSS, which can be driven by a combination of different models such as document-driven, communication driven and knowledge drive.

Web-based DSS are computerized systems that delivers decision support information or decision support tools to a manager or business analyst using a "thin-client" Web browser like Netscape Navigator or Internet Explorer.

Database design and management is a complex and often ill-conceived topic and extends well beyond the scope of this paper. Consequently our discussion on databases will parsimonious at best.

A good DBMS ensures data integrity, reduces data redundancy, follows a logical sequence and are consistent in its performance. Relational databases are in most cases the system of choice when it comes to designing a DSS.

This is primarily because of the flexibility associated with a relational DB but also because it allows normalization reduction in data duplicationwhich helps with maintenance of a large database.

Some hierarchical and network databases are still being used today, but merely so because of the costs involved with migrating to a totally new DB platform overshadows the maintenance costs of legacy systems — even this is gradually changing as more organizations are putting additional requirements on their DSS, underscoring the need for a more suitable DB design.

Because the database constitutes such a vital link in the capabilities of any DSS its structure and design should be carefully evaluated and implemented with due concern of applications build on top of it.

  1. Thus the data warehouse and the DB coexists to provide synergistic outcomes which supports information requirement of the DSS superimposed on the systems platform. Consequently our discussion on databases will parsimonious at best.
  2. In other words, one needs a set of principles, models, templates or other abstractions. The rapid expansion of the Internet provided additional opportunities for the scope of Decision Support Systems and consequently many new innovative systems such as OLAP and other web-drive systems were developed.
  3. Antibiotic prescribing in critical care is a complex, cognitively demanding task, made under time pressure. Thus, in order to comprehend the intricate complexities of what services a DSS can provide, we first have to look at the stand-alone units that supports the DSS.
  4. This not only decreases the complexity of the decision process, but also decreases the processing load on the manager and leaves him with more time to focus on critical and situation-relevant information segments. Document driven These systems help managers retrieve and mange unstructured documents and web pages by integrating a variety of storage and processing technologies to provide complete document retrieval and analysis.

There seems to be a general trend in recent years, to either migrate to web-based DSS, using a task specific search-engine, or to build the DSS around a thin client fat server environment, using network and web-based technologies. This is perhaps a further justification of the need for an advanced database system since the Internet is the most comprehensive network of thousands of interconnected databases and web pages. The Data Warehouse DB generally provides current information about the organization relating to the underlying transactional processes, but it fails to provide historical, contend rich information that are often more important to the decision making process than stand alone islands of information.

The data warehouse fills this gap by capturing operational data and presenting it in a more meaningful format, using a relational database, and ultimately complimenting the functions of the DB used in the DSS. Thus the data warehouse and the DB coexists to provide synergistic outcomes which supports information requirement of the DSS superimposed on the systems platform.

Management requirements of the Decision Support Systems Information uses and requirements differ at each managerial level.

  • The data warehouse fills this gap by capturing operational data and presenting it in a more meaningful format, using a relational database, and ultimately complimenting the functions of the DB used in the DSS;
  • Thus the data warehouse and the DB coexists to provide synergistic outcomes which supports information requirement of the DSS superimposed on the systems platform;
  • They define DSS broadly as an interactive computer based system that help decision-makers use data and models to solve ill-structured, unstructured or semi-structured problems;
  • In addition to that, DSS can take on many different forms and can be used in many different ways, i;
  • GDSS supports electronic communication, scheduling, document sharing and other group productivity and decision enhancing activities and involves technologies such as two-way interactive video, bulletin boards, e-mail, etc;
  • GDSS supports electronic communication, scheduling, document sharing and other group productivity and decision enhancing activities and involves technologies such as two-way interactive video, bulletin boards, e-mail, etc.

Top management commonly use information to make decisions about long term planning and thus analyze long term trend information to make their decisions Gore et al, 1984. At lower management levels, information emphasis is internally generated and relies on short-term goals. However, reports and analyses generated by lower management teams are often used in decision-making efforts of top management, and it is thus crucial that the DSS supports the lower management just as much as it lends itself to top management.

In short, the information needs for different levels of management are directed towards supervisory functions for lower management, tactical decision making for middle management and strategic decision making for top management. See appendix 1 for a more elaborate outline of information requirements of managers. Moreover, because the DSS are employed to improve management control, it should address the primary tasks of management control.